Mohammad Najibullah (محمد نجيب الله)

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Agent-Based Modelling and Geographical Information Systems Github Repo : github.com/abmgis/abmgis Covers: - Agent-based Modelling and Geographical Information Systems - Introduction to Agent-Based Modelling - Designing and Developing An Agent-Based Model - Building Agent-Based Models with NetLogo - Fundamentals of Geographical Information Systems - Integrating Agent-Based Modelling and GIS - Modelling Human Behaviour - Networks - Spatial Statistics - Evaluating Our Models: Verification, Calibration, Validation - Alternative Modelling Approaches - Summary and Outlook

I struggled with AI engineering until I learned these 10 concepts (not joking): 1 How RAG Works ↳ newsletter.systemdesign.one/p/how-rag-works 2 LLM Concepts - A Deep Dive ↳ newsletter.systemdesign.one/p/llm-concepts 3 How to Design an AI Agent ↳ newsletter.systemdesign.one/p/how-do-ai-ag… 4 What is Reinforcement Learning ↳ newsletter.systemdesign.one/p/what-is-rein… 5 Context Engineering vs Prompt Engineering ↳ newsletter.systemdesign.one/p/context-engi… 6 Context Engineering 101 ↳ newsletter.systemdesign.one/p/what-is-cont… 7 AI Coding Workflow 101 ↳ newsletter.systemdesign.one/p/ai-coding-wo… 8 How ChatGPT Apps Work ↳ newsletter.systemdesign.one/p/apps-in-chat… 9 How AI Agents Work ↳ newsletter.systemdesign.one/p/ai-agents-ex… 10 How MCP Works ↳ newsletter.systemdesign.one/p/how-mcp-works What else should make this list? —— 👋 PS - Want my System Design Playbook for FREE? Join my newsletter with 200K+ software engineers: → newsletter.systemdesign.one/join ——— 💾 Save this for later & RT to help others learn AI engineering. 👤 Follow @systemdesignone + turn on notifications.

Solve 1 less leetcode question, but spend that time watching this Goated playlist. DSA teaches you how to solve problems. System design teaches you how real world systems actually work at scale. This opens up the mind to think beyond a DSA problem and go deep within the system and how real world things happen at such a large scale

CPU vs GPU vs TPU vs NPU vs LPU, explained visually: 5 hardware architectures power AI today. Each one makes a fundamentally different tradeoff between flexibility, parallelism, and memory access. > CPU It is built for general-purpose computing. A few powerful cores handle complex logic, branching, and system-level tasks. It has deep cache hierarchies and off-chip main memory (DRAM). It's great for operating systems, databases, and decision-heavy code, but not that great for repetitive math like matrix multiplications. > GPU Instead of a few powerful cores, GPUs spread work across thousands of smaller cores that all execute the same instruction on different data. This is why GPUs dominate AI training. The parallelism maps directly to the kind of math neural networks need. > TPU They go one step further with specialization. The core compute unit is a grid of multiply-accumulate (MAC) units where data flows through in a wave pattern. Weights enter from one side, activations from the other, and partial results propagate without going back to memory each time. The entire execution is compiler-controlled, not hardware-scheduled. Google designed TPUs specifically for neural network workloads. > NPU This is an edge-optimized variant. The architecture is built around a Neural Compute Engine packed with MAC arrays and on-chip SRAM, but instead of high-bandwidth memory (HBM), NPUs use low-power system memory. The design goal is to run inference at single-digit watt power budgets, like smartphones, wearables, and IoT devices. Apple Neural Engine and Intel's NPU follow this pattern. > LPU (Language Processing Unit) This is the newest entrant, by Groq. The architecture removes off-chip memory from the critical path entirely. All weight storage lives in on-chip SRAM. Execution is fully deterministic and compiler-scheduled, which means zero cache misses and zero runtime scheduling overhead. The tradeoff is that it provides limited memory per chip, which means you need hundreds of chips linked together to serve a single large model. But the latency advantage is real. AI compute has evolved from general-purpose flexibility (CPU) to extreme specialization (LPU). Each step trades some level of generality for efficiency. The visual below maps the internal architecture of all five side by side, and it was inspired by ByteByteGo's post on CPU vs GPU vs TPU. I expanded it to include two more architectures that are becoming central to AI inference today. 👉 Over to you: Which of these 5 have you actually worked with or deployed on? ____ Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

You are a genius if you know all of this. If not, tutorials are included! AI Agents – youtube.com/watch?v=OhI005… Context – youtube.com/watch?v=4GiqzU… MCP – youtube.com/watch?v=VfZlgl… Claude Code – youtube.com/watch?v=SUysp3… APIs – youtube.com/watch?v=WXsD0Z… Cursor – youtube.com/watch?v=2aldTx… Prompts – youtube.com/watch?v=qBlX6F… OpenClaw – youtube.com/watch?v=n1sfrc… Free AI agents resources - github.com/avinash201199/… .

If you're prepping for AI/ML engineer interviews, bookmark this now A free GitHub repo with 300+ Q&As covering: ◾️ LLM fundamentals ◾️ RAG pipelines ◾️ AI agents & MCP ◾️ Fine-tuning (LoRA, QLoRA, RLHF) ◾️ Vector DBs & embeddings ◾️ LLMOps & production AI ◾️ AI safety & ethics ◾️ System design questions covers roles like AI engineer, LLMOps, MLOps, AI solutions architect and more github.com/amitshekhariit…

Every man should read: Day 1 The art of war Day 2 Declutter your mind Day 3 Rich Dad Poor Dad Day 4 Unlimited memory Day 5 The 80/20 principle Day 6 Thinking, fast and slow Day 7 The 48 laws of power Day 8 hink and grow rich Day 9 The intelligent investor Day 10 The 4-hour workweek Day 11 The law of attraction Day 12 How to win friends Day 13 The power of habit Day 14 The 5 second rule Day 15 Mindset Day 16 12 rules for life Day 17 No excuses Day 19 The 5 am club

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Deep Learning: Concepts, Architectures, and Applications clcoding.com/2026/03/deep-l…

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